In [1]:
import os

## Set directory
os.chdir('/hpc/group/pbenfeylab/CheWei/CW_data/genesys')

import networkx as nx
from genesys_evaluate_v1 import *
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
# Suppress all warning messages
warnings.filterwarnings("ignore", category=DeprecationWarning)

/hpc/group/pbenfeylab/ch416/miniconda3/envs/genesys/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
In [2]:
## Conda Env genesys on DCC
print(torch.__version__)
print(sc.__version__) 

1.11.0
1.9.6
In [3]:
## Genes considered/used (shared among samples) 
gene_list = pd.read_csv('./gene_list_1108.csv')

Load Data¶

In [4]:
with open("./genesys_root_data.pkl", 'rb') as file_handle:
    data = pickle.load(file_handle)
    
batch_size = 2000
#dataset = Root_Dataset(data['X_test'], data['y_test'])
#loader = DataLoader(dataset,
#                         batch_size = batch_size,
#                         shuffle = True, drop_last=True)

X_all = np.vstack((data['X_train'],data['X_val'],data['X_test']))
y_all = pd.concat((data['y_train'],data['y_val'],data['y_test']))
dataset = Root_Dataset(X_all, y_all)
loader = DataLoader(dataset,
                         batch_size = batch_size,
                         shuffle = True, drop_last=True)
In [5]:
classes = ['Columella', 'Lateral Root Cap', 'Phloem', 'Xylem', 'Procambium', 'Pericycle', 'Endodermis', 'Cortex', 'Atrichoblast', 'Trichoblast']
class2num = {c: i for (i, c) in enumerate(classes)}
num2class = {i: c for (i, c) in enumerate(classes)}
In [6]:
cts = ['Atrichoblast','Trichoblast','Cortex','Endodermis','Pericycle','Procambium','Xylem','Phloem','Lateral Root Cap','Columella']
ctw = np.zeros((len(cts), 17513, 17513))
## number of cells sampled from the atlas
batch_size = 2000

Extract a sample¶

In [7]:
sample = next(iter(loader))
xo = sample['x'].to(device)
y = sample['y'].to(device)
y_label = [num2class[i] for i in y.tolist()]
In [8]:
xo.shape
Out[8]:
torch.Size([2000, 11, 17513])
In [9]:
len(y_label)
Out[9]:
2000
In [10]:
len(loader)
Out[10]:
55
In [11]:
## GRN for the transition t7 to t9
for ct in cts:
    print(ct)
    cws = np.zeros((len(loader), 17513, 17513))
    with torch.no_grad():
        for i, sample in enumerate(loader):
            x = sample['x'].to(device)
            y = sample['y'].to(device)
            y_label = [num2class[i] for i in y.tolist()]
            
            #pred_h = model.init_hidden(batch_size)
            #tfrom = model.generate_current(x, pred_h, 0).to('cpu').detach().numpy()
            #cfrom = tfrom[np.where(np.array(y_label)==ct)[0],:]
            cfrom = x[np.where(np.array(y_label)==ct)[0],7,:] 
            
            #pred_h = model.init_hidden(batch_size)
            #tto = model.generate_next(x, pred_h, 0).to('cpu').detach().numpy()   
            #cto = tto[np.where(np.array(y_label)==ct)[0],:]
            cto = x[np.where(np.array(y_label)==ct)[0],9,:]
            
            cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()
            cws[i] = cw
    
    ## Calculate mean across number of repeats
    cwm = np.mean(cws, axis=0)
    ctw[cts.index(ct)] = cwm

Atrichoblast

/tmp/ipykernel_984347/1398938877.py:21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  cw = torch.linalg.lstsq(torch.tensor(cfrom), torch.tensor(cto)).solution.detach().numpy()

Trichoblast
Cortex
Endodermis
Pericycle
Procambium
Xylem
Phloem
Lateral Root Cap
Columella
In [12]:
# Save the array to disk
np.save('genesys_raw_ctw_t7-t9.npy', ctw)
In [11]:
ctw = np.load('genesys_raw_ctw_t7-t9.npy')
In [12]:
## Calculate z-scores
ctw_z = np.zeros((len(cts), 17513, 17513))
for i in range(len(cts)):
    if np.std(ctw[i])==0:
        ctw_z[i] = np.zeros((17513, 17513))
    else:
        ctw_z[i] = (ctw[i] - np.mean(ctw[i])) / np.std(ctw[i])
In [13]:
## Filtering based on z-scores (with no weights)
ctw_f = np.zeros((len(cts), 17513, 17513))
## z-score threshold (keep values > mean + threshold*std)
threshold=3
for i in range(len(cts)):
    ctw_f[i] = np.abs(ctw_z[i]) > threshold

Load TFs list¶

In [14]:
wanted_TFs = pd.read_csv("./Kay_TF_thalemine_annotations.csv")
In [15]:
## Make TF names unique and assign preferred names
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G33880"]="WOX9"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G45160"]="SCL27"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G04410"]="NAC78"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G29035"]="ORS1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02540"]="ZHD3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G16500"]="IAA26"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G09740"]="HAG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G24660"]="ZHD2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G46880"]="HDG5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G28420"]="RLT1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G14580"]="BLJ"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G45260"]="BIB"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G02070"]="RVN"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G28160"]="FIT"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G68360"]="GIS3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G20640"]="NLP4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G05550"]="VFP5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G59470"]="FRF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G15150"]="HAT7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G14750"]="WER"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G75710"]="BRON"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G74500"]="TMO7"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT2G12646"]="RITF1"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT3G48100"]="ARR5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G16141"]="GATA17L"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G65640"]="NFL"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G62700"]="VND5"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT4G36160"]="VND2"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G66300"]="VND3"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT1G12260"]="VND4"
wanted_TFs['Name'][wanted_TFs['GeneID']=="AT5G62380"]="VND6"
In [16]:
pd.Series(wanted_TFs['Name']).value_counts().head(5)
Out[16]:
Name
NAC001    1
PRE5      1
MYB118    1
MYB21     1
MYB0      1
Name: count, dtype: int64

Network Analysis¶

In [17]:
TFidx = []
for i in wanted_TFs['GeneID']:
    if i in gene_list['features'].tolist():
        TFidx.append(np.where(gene_list['features']==i)[0][0])

TFidx = np.sort(np.array(TFidx))
In [19]:
def network(i):
    ## No weights
    adj_nw = ctw_f[i]
    ## Weighted
    adj = ctw[i]*ctw_f[i]
    ## TF only
    adj = adj[np.ix_(TFidx,TFidx)]
    adj_nw = adj_nw[np.ix_(TFidx,TFidx)]
    
    ## Remove no connect 
    regidx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[0]).value_counts().index[pd.Series(np.where(adj_nw==True)[0]).value_counts()>=1]))
    taridx = np.sort(np.array(pd.Series(np.where(adj_nw==True)[1]).value_counts().index[pd.Series(np.where(adj_nw==True)[1]).value_counts()>=1]))
    ## Reciprocol
    keepidx = np.sort(np.array(list(set(regidx).intersection(taridx))))
    #keepidx = np.sort(np.array(list(set(regidx).union(taridx))))
    
    TFID = np.array(gene_list['features'][TFidx])[keepidx].tolist()
    ## TF name to keep
    TFname = []
    for i in np.array(gene_list['features'][TFidx])[keepidx]:
        TFname.append(wanted_TFs['Name'][np.where(wanted_TFs['GeneID']==i)[0][0]])
        
    adj = adj[np.ix_(keepidx,keepidx)]
    
    # Create a NetworkX graph for non-directed edges
    G = nx.Graph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=abs(weight), distance=1/abs(weight))
            

    ## Measures the extent to which how close a node is to all other nodes in the network, considering the shortest paths or geodesic distances between nodes
    closeness_centrality = nx.closeness_centrality(G, distance='distance')
    ## Measures the extent to which a node that are not only well-connected but also connected to other well-connected nodes.
    eigenvector_centrality = nx.eigenvector_centrality(G)
    
    # Create a NetworkX graph for diected edges
    G = nx.MultiDiGraph()  # supports directed edges and allows for multiple edges between the same pair of nodes
    
    # Add nodes to the graph
    num_nodes = adj.shape[0]
    for i, name in enumerate(TFname):
        G.add_node(i, name=name)
    
    # Add edges to the graph with weights
    for i in range(num_nodes):
        for j in range(num_nodes):
            weight = adj[i, j]
            if weight != 0:
                G.add_edge(j, i, weight=weight)
    
    ## Measures the number of connections (edges) each node has
    degree_centrality = nx.degree_centrality(G)
    # Calculate outgoing centrality
    out_centrality = nx.out_degree_centrality(G)
    # Calculate incoming centrality
    in_centrality = nx.in_degree_centrality(G)
    ## Measures the extent to which a node lies on the shortest paths between other nodes.
    betweenness_centrality = nx.betweenness_centrality(G, weight='weight')
    
    ## Non_Reciprocal Out centrality
    # Visualize the graph
    pos = nx.spring_layout(G)  # Positions of the nodes
    
    # Node colors based on weighted betweenness centrality
    node_colors = [out_centrality[node] for node in G.nodes()]
    
    # Node sizes based on weighted betweenness centrality
    node_sizes = [out_centrality[node] * 1000 for node in G.nodes()]

    # Get the edge weights as a dictionary
    edge_weights = nx.get_edge_attributes(G, 'weight')
    edge_colors = ['red' if weight > 0 else 'blue' for (_, _, weight) in G.edges(data='weight')]
    
    # Scale the edge weights to desired linewidths
    max_weight = max(edge_weights.values())
    edge_widths = [float(edge_weights[edge]) / max_weight for edge in G.edges]
    
    # Draw the graph
    nx.draw(G, pos=pos, node_color=node_colors, node_size=node_sizes, with_labels=False, width=edge_widths, edge_color=edge_colors)
    # Add node labels
    labels = {node: G.nodes[node]['name'] for node in G.nodes}
    nx.draw_networkx_labels(G, pos=pos, labels=labels, font_size=8)
    
    # Add a colorbar to show the weighted betweenness centrality color mapping
    sm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=min(node_colors), vmax=max(node_colors)))
    sm.set_array([])
    plt.colorbar(sm)
    
    # Show the plot
    plt.show()
    
    dc = pd.DataFrame.from_dict(degree_centrality, orient='index', columns=['degree_centrality'])
    oc = pd.DataFrame.from_dict(out_centrality, orient='index', columns=['out_centrality'])
    ic = pd.DataFrame.from_dict(in_centrality, orient='index', columns=['in_centrality'])
    bc = pd.DataFrame.from_dict(betweenness_centrality, orient='index', columns=['betweenness_centrality'])
    cc = pd.DataFrame.from_dict(closeness_centrality, orient='index', columns=['closeness_centrality'])
    ec = pd.DataFrame.from_dict(eigenvector_centrality, orient='index', columns=['eigenvector_centrality'])
    df = pd.concat([dc,oc,ic,bc,cc,ec], axis=1)
    df.index =TFname
    df = df.sort_values('betweenness_centrality', ascending=False)
    
    return(df)
In [20]:
atri = network(0)
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In [21]:
atri
Out[21]:
degree_centrality out_centrality in_centrality betweenness_centrality closeness_centrality eigenvector_centrality
TGA10 1.077088 0.595289 0.481799 0.967931 0.000342 0.182885
IAA1 0.126338 0.040685 0.085653 0.955253 0.000240 0.059468
PHE1 0.057816 0.029979 0.027837 0.954554 0.000180 0.029390
LBD25 0.391863 0.261242 0.130621 0.953971 0.000252 0.109889
AT3G21330 0.094218 0.059957 0.034261 0.953824 0.000202 0.041413
... ... ... ... ... ... ...
JMJ18 0.008565 0.004283 0.004283 0.000000 0.000178 0.008915
AT1G62310 0.008565 0.004283 0.004283 0.000000 0.000149 0.005849
AT5G07810 0.025696 0.019272 0.006424 0.000000 0.000162 0.016625
FBH3 0.064240 0.017131 0.047109 0.000000 0.000229 0.039612
SAP7 0.006424 0.004283 0.002141 0.000000 0.000163 0.004699

468 rows × 6 columns

In [22]:
tri = network(1)
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In [23]:
cor = network(2)
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In [24]:
end = network(3)
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In [25]:
per = network(4)
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In [26]:
pro = network(5)
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In [27]:
xyl = network(6)
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In [28]:
phl = network(7)
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In [29]:
lrc = network(8)
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In [30]:
col = network(9)
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In [31]:
atri.columns = ['atri_degree_centrality','atri_out_centrality','atri_in_centrality','atri_betweenness_centrality','atri_closeness_centrality','atri_eigenvector_centrality']
tri.columns = ['tri_degree_centrality','tri_out_centrality','tri_in_centrality','tri_betweenness_centrality','tri_closeness_centrality','tri_eigenvector_centrality']
cor.columns = ['cor_degree_centrality','cor_out_centrality','cor_in_centrality','cor_betweenness_centrality','cor_closeness_centrality','cor_eigenvector_centrality']
end.columns = ['end_degree_centrality','end_out_centrality','end_in_centrality','end_betweenness_centrality','end_closeness_centrality','end_eigenvector_centrality']
per.columns = ['per_degree_centrality','per_out_centrality','per_in_centrality','per_betweenness_centrality','per_closeness_centrality','per_eigenvector_centrality']
pro.columns = ['pro_degree_centrality','pro_out_centrality','pro_in_centrality','pro_betweenness_centrality','pro_closeness_centrality','pro_eigenvector_centrality']
xyl.columns = ['xyl_degree_centrality','xyl_out_centrality','xyl_in_centrality','xyl_betweenness_centrality','xyl_closeness_centrality','xyl_eigenvector_centrality']
phl.columns = ['phl_degree_centrality','phl_out_centrality','phl_in_centrality','phl_betweenness_centrality','phl_closeness_centrality','phl_eigenvector_centrality']
lrc.columns = ['lrc_degree_centrality','lrc_out_centrality','lrc_in_centrality','lrc_betweenness_centrality','lrc_closeness_centrality','lrc_eigenvector_centrality']
col.columns = ['col_degree_centrality','col_out_centrality','col_in_centrality','col_betweenness_centrality','col_closeness_centrality','col_eigenvector_centrality']
In [32]:
## Indentify main regulators in each net work
tff = []
tff = tff + atri[atri['atri_betweenness_centrality']>0].index.tolist()
tff = tff + tri[tri['tri_betweenness_centrality']>0].index.tolist()
tff = tff + lrc[lrc['lrc_betweenness_centrality']>0].index.tolist()
tff = tff + cor[cor['cor_betweenness_centrality']>0].index.tolist()
tff = tff + end[end['end_betweenness_centrality']>0].index.tolist()
tff = tff + per[per['per_betweenness_centrality']>0].index.tolist()
tff = tff + pro[pro['pro_betweenness_centrality']>0].index.tolist()
tff = tff + xyl[xyl['xyl_betweenness_centrality']>0].index.tolist()
tff = tff + phl[phl['phl_betweenness_centrality']>0].index.tolist()
tff = tff + col[col['col_betweenness_centrality']>0].index.tolist()
tf_occurance = pd.DataFrame(pd.Series(tff).value_counts())
tf_occurance = tf_occurance.rename(columns={
    'count': 'tf_occurance'
})
tf_spec = pd.concat([tf_occurance, atri, tri, lrc, cor, end, per, pro, xyl, phl, col], axis=1)
tf_spec = tf_spec.fillna(0)
In [33]:
## Epidermis (atri, tri, lrc)
celltype1='atri'
celltype2='tri'
celltype3='lrc'
ts = tf_spec[tf_spec['tf_occurance']==3][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==9].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[33]:
atri_betweenness_centrality tri_betweenness_centrality lrc_betweenness_centrality atri_out_centrality tri_out_centrality lrc_out_centrality atri_in_centrality tri_in_centrality lrc_in_centrality centrality_count centrality_sum
In [34]:
## atri, tri
celltype1='atri'
celltype2='tri'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[34]:
atri_betweenness_centrality tri_betweenness_centrality atri_out_centrality tri_out_centrality atri_in_centrality tri_in_centrality centrality_count centrality_sum
LRL3 0.000005 0.319964 0.006424 0.965909 0.004283 0.920455 6 8.217039
AT3G05860 0.779462 0.007809 0.284797 0.009470 0.331906 0.049242 6 7.462685
AT5G06800 0.004949 0.552962 0.029979 0.331439 0.072805 0.134470 6 7.126604
Rap2.6L 0.128820 0.000783 0.171306 0.041667 0.306210 0.160985 6 6.809771
MC2 0.091562 0.009958 0.149893 0.217803 0.092077 0.123106 6 6.684400
HB24 0.226406 0.001362 0.137045 0.009470 0.149893 0.013258 6 6.537434
HFR1 0.000482 0.011788 0.044968 0.109848 0.132762 0.030303 6 6.330152
NAC003 0.002063 0.000173 0.092077 0.047348 0.074946 0.062500 6 6.279108
In [35]:
## Atrichoblast specific
celltype = 'atri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[35]:
atri_betweenness_centrality atri_out_centrality atri_in_centrality centrality_count centrality_sum
PHE1 0.954554 0.029979 0.027837 3 4.012370
AT3G21330 0.953824 0.059957 0.034261 3 4.048042
WRKY13 0.947124 0.102784 0.128480 3 4.178387
AT2G28710 0.942382 0.353319 0.269807 3 4.565508
MEA 0.453713 0.130621 0.132762 3 3.717097
AT4G38070 0.294368 0.017131 0.006424 3 3.317923
GATA17 0.212529 0.029979 0.008565 3 3.251073
GL2 0.105398 0.072805 0.164882 3 3.343086
AT4G31650 0.098418 0.021413 0.023555 3 3.143386
AT5G26749 0.049682 0.010707 0.017131 3 3.077520
ULT2 0.022980 0.062099 0.040685 3 3.125764
AT2G24690 0.007577 0.025696 0.128480 3 3.161753
AT1G14600 0.004071 0.008565 0.102784 3 3.115420
NAC044 0.003138 0.027837 0.014989 3 3.045965
AT4G01350 0.002573 0.085653 0.044968 3 3.133194
AT3G13840 0.002141 0.002141 0.042827 3 3.047109
AT1G63840 0.001595 0.074946 0.042827 3 3.119368
TTG2 0.001130 0.040685 0.092077 3 3.133893
EMB1789 0.001002 0.004283 0.004283 3 3.009567
AT3G16280 0.000800 0.023555 0.055675 3 3.080029
WOL 0.000708 0.029979 0.049251 3 3.079937
ZFN3 0.000565 0.014989 0.019272 3 3.034826
AT2G06025 0.000505 0.085653 0.021413 3 3.107572
GATA26 0.000427 0.021413 0.044968 3 3.066809
KELP 0.000110 0.004283 0.006424 3 3.010817
ALY1 0.000009 0.014989 0.014989 3 3.029988
In [36]:
## Trichoblast specific
celltype = 'tri'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[36]:
tri_betweenness_centrality tri_out_centrality tri_in_centrality centrality_count centrality_sum
RAP2.11 0.899341 0.214015 0.034091 3 4.147447
EIL2 0.898780 0.096591 0.022727 3 4.018098
AT5G56200 0.892592 0.225379 0.073864 3 4.191834
RSL2 0.892301 0.740530 0.242424 3 4.875255
RSL4 0.877641 0.412879 0.111742 3 4.402263
4-Sep 0.841628 0.009470 0.005682 3 3.856779
HAT9 0.567244 0.015152 0.005682 3 3.588077
RHD6 0.545361 0.494318 0.340909 3 4.380588
AT1G02040 0.530274 0.005682 0.005682 3 3.541638
AT2G20030 0.475073 0.215909 0.034091 3 3.725073
NAC016 0.208243 0.009470 0.024621 3 3.242334
AT4G09100 0.115865 0.407197 0.337121 3 3.860183
AT2G28920 0.114053 0.003788 0.003788 3 3.121629
AT5G65130 0.109335 0.037879 0.007576 3 3.154789
AT2G05160 0.068552 0.236742 0.123106 3 3.428400
AT1G18335 0.013150 0.017045 0.007576 3 3.037771
GL3 0.006314 0.013258 0.020833 3 3.040405
AT2G14760 0.003479 0.028409 0.013258 3 3.045145
AT2G37120 0.001959 0.018939 0.102273 3 3.123171
AT2G01060 0.000367 0.024621 0.017045 3 3.042033
ABF3 0.000032 0.005682 0.022727 3 3.028441
AT2G39000 0.000022 0.022727 0.020833 3 3.043582
NAC084 0.000007 0.013258 0.034091 3 3.047356
In [37]:
## LRC specific
celltype = 'lrc'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[37]:
lrc_betweenness_centrality lrc_out_centrality lrc_in_centrality centrality_count centrality_sum
OFP6 0.843085 0.199659 0.116041 3 4.158785
bZIP52 0.795344 0.015358 0.010239 3 3.820942
AT3G46070 0.312657 0.003413 0.011945 3 3.328016
AGL17 0.147230 0.008532 0.046075 3 3.201838
BNQ3 0.033138 0.061433 0.029010 3 3.123582
REF6 0.030606 0.003413 0.023891 3 3.057910
AT4G14720 0.013731 0.013652 0.023891 3 3.051273
HMGB5 0.007754 0.001706 0.040956 3 3.050416
BZIP34 0.003378 0.027304 0.035836 3 3.066518
HB4 0.002879 0.008532 0.032423 3 3.043835
LSMT-L 0.002135 0.001706 0.027304 3 3.031146
AT1G58220 0.001701 0.003413 0.010239 3 3.015353
AL6 0.000977 0.011945 0.029010 3 3.041933
SDIR1 0.000744 0.001706 0.017065 3 3.019515
ZF1 0.000738 0.005119 0.008532 3 3.014390
PYE 0.000651 0.032423 0.042662 3 3.075736
AT3G19080 0.000598 0.001706 0.005119 3 3.007424
HAM3 0.000478 0.104096 0.052901 3 3.157475
AT5G41580 0.000146 0.008532 0.034130 3 3.042808
AT1G26680 0.000012 0.001706 0.030717 3 3.032435
TLP3 0.000012 0.010239 0.090444 3 3.100694
AT5G58340 0.000012 0.001706 0.013652 3 3.015370
VOZ2 0.000006 0.001706 0.022184 3 3.023897
In [38]:
## Columella specific
celltype = 'col'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[38]:
col_betweenness_centrality col_out_centrality col_in_centrality centrality_count centrality_sum
SUVH6 0.245093 0.018519 0.107143 3 3.370754
SMZ 0.064750 0.042328 0.063492 3 3.170570
MYB4R1 0.038030 0.047619 0.062169 3 3.147819
NAM 0.030315 0.455026 0.212963 3 3.698304
AT1G77570 0.016572 0.018519 0.091270 3 3.126360
... ... ... ... ... ...
bHLH11 0.000005 0.002646 0.015873 3 3.018524
AIL5 0.000004 0.006614 0.006614 3 3.013231
MBD10 0.000004 0.023810 0.047619 3 3.071432
SPL13A 0.000002 0.007937 0.001323 3 3.009261
HD2C 0.000002 0.010582 0.033069 3 3.043653

134 rows × 5 columns

In [39]:
## Ground tissue
celltype1='cor'
celltype2='end'
ts = tf_spec[tf_spec['tf_occurance']==2][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==6].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[39]:
cor_betweenness_centrality end_betweenness_centrality cor_out_centrality end_out_centrality cor_in_centrality end_in_centrality centrality_count centrality_sum
MYB122 0.069754 0.735343 0.165394 0.285307 0.058524 0.206847 6 7.521170
IDD4 0.003375 0.049073 0.147583 0.057061 0.106870 0.042796 6 6.406758
JKD 0.005057 0.000919 0.104326 0.084165 0.078880 0.102710 6 6.376058
ABA1 0.007205 0.000004 0.045802 0.019971 0.043257 0.048502 6 6.164741
In [40]:
## Cortex specific
celltype = 'cor'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[40]:
cor_betweenness_centrality cor_out_centrality cor_in_centrality centrality_count centrality_sum
IDD7 0.782923 0.129771 0.053435 3 3.966129
AT3G51120 0.390877 0.010178 0.002545 3 3.403600
AT1G72210 0.296055 0.198473 0.315522 3 3.810050
CRF5 0.156969 0.002545 0.002545 3 3.162058
AT2G37000 0.041414 0.005089 0.017812 3 3.064314
AT2G38300 0.040855 0.096692 0.259542 3 3.397089
RGL3 0.040011 0.012723 0.137405 3 3.190139
COL4 0.005881 0.109415 0.099237 3 3.214532
PIL5 0.005134 0.010178 0.002545 3 3.017857
MYB14 0.002519 0.005089 0.068702 3 3.076310
ZFN1 0.001792 0.119593 0.063613 3 3.184998
ERF15 0.001448 0.017812 0.053435 3 3.072694
SIGF 0.000227 0.002545 0.025445 3 3.028217
BPC4 0.000039 0.007634 0.005089 3 3.012762
In [41]:
## Endodermis specific
celltype = 'end'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[41]:
end_betweenness_centrality end_out_centrality end_in_centrality centrality_count centrality_sum
chr31 0.974708 0.512126 0.228245 3 4.715078
RVN 0.914977 0.205421 0.017118 3 4.137516
AT1G03040 0.913301 0.116976 0.098431 3 4.128708
MYB36 0.766462 0.924394 0.901569 3 5.592425
bZIP58 0.685637 0.315264 0.191155 3 4.192056
AGL102 0.467997 0.359486 0.057061 3 3.884545
AT2G46810 0.437779 0.047076 0.001427 3 3.486281
BLJ 0.186350 0.185449 0.246790 3 3.618590
AT4G38340 0.177084 0.209700 0.057061 3 3.443846
SCR 0.134273 0.105563 0.057061 3 3.296898
SIGA 0.093587 0.148359 0.027104 3 3.269050
MYB68 0.077120 0.184023 0.365193 3 3.626336
MYB3 0.019295 0.021398 0.189729 3 3.230422
AT2G33720 0.004540 0.125535 0.012839 3 3.142914
AT4G00940 0.003104 0.176890 0.152639 3 3.332633
AT5G41920 0.002317 0.085592 0.025678 3 3.113587
AT2G38090 0.000467 0.024251 0.011412 3 3.036130
AT4G00390 0.000247 0.001427 0.007133 3 3.008806
ERF3 0.000122 0.032810 0.031384 3 3.064316
ATU2AF35A 0.000084 0.009986 0.012839 3 3.022908
AP3 0.000053 0.024251 0.012839 3 3.037143
AT4G11680 0.000033 0.094151 0.015692 3 3.109876
ZIM 0.000012 0.005706 0.012839 3 3.018557
BIB 0.000004 0.074180 0.009986 3 3.084170
YY1 0.000002 0.002853 0.014265 3 3.017120
In [42]:
## Stele
celltype1='per'
celltype2='pro'
celltype3='xyl'
celltype4='phl'
ts = tf_spec[tf_spec['tf_occurance']==4][[celltype1+'_betweenness_centrality', celltype2+'_betweenness_centrality', celltype3+'_betweenness_centrality', celltype4+'_betweenness_centrality', celltype1+'_out_centrality', celltype2+'_out_centrality', celltype3+'_out_centrality', celltype4+'_out_centrality', celltype1+'_in_centrality', celltype2+'_in_centrality', celltype3+'_in_centrality', celltype4+'_in_centrality']]
tso = (ts > 0)
ts['centrality_count'] = tso.sum(axis=1)
ts['centrality_sum'] = ts.sum(axis=1)
ts[ts['centrality_count']==12].sort_values(['centrality_count','centrality_sum'], ascending=False)
Out[42]:
per_betweenness_centrality pro_betweenness_centrality xyl_betweenness_centrality phl_betweenness_centrality per_out_centrality pro_out_centrality xyl_out_centrality phl_out_centrality per_in_centrality pro_in_centrality xyl_in_centrality phl_in_centrality centrality_count centrality_sum
TMO6 0.770435 0.535439 0.825228 0.177098 0.091644 0.038961 0.043956 0.187500 0.066038 0.038961 0.021978 0.310049 12 15.107286
MYB20 0.956377 0.978269 0.019980 0.000069 0.208895 0.266955 0.057692 0.002451 0.318059 0.180375 0.068681 0.040441 12 15.098246
IAA12 0.042892 0.882128 0.605909 0.000275 0.004043 0.676768 0.085165 0.051471 0.005391 0.233766 0.016484 0.030637 12 14.634929
In [43]:
## Pericycle
celltype = 'per'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[43]:
per_betweenness_centrality per_out_centrality per_in_centrality centrality_count centrality_sum
ZFP7 0.960866 0.020216 0.016173 3 3.997254
BHLH32 0.953445 0.006739 0.028302 3 3.988485
HB21 0.931609 0.355795 0.121294 3 4.408698
AT1G26790 0.904322 0.115903 0.036388 3 4.056613
HAT2 0.812881 0.138814 0.070081 3 4.021776
GAI 0.730724 0.036388 0.029650 3 3.796761
DEL2 0.521156 0.008086 0.004043 3 3.533285
LBD39 0.419987 0.028302 0.032345 3 3.480634
bZIP4 0.210199 0.099730 0.036388 3 3.346318
ING2 0.110154 0.006739 0.002695 3 3.119588
AT1G64620 0.088969 0.032345 0.056604 3 3.177918
ABO3 0.083565 0.002695 0.024259 3 3.110519
ATL5 0.074912 0.216981 0.049865 3 3.341758
GATA16 0.068515 0.039084 0.053908 3 3.161507
AT1G61990 0.068153 0.001348 0.012129 3 3.081630
NF-YB11 0.065785 0.002695 0.012129 3 3.080610
AT4G35270 0.052564 0.133423 0.092992 3 3.278979
CHR18 0.046173 0.008086 0.005391 3 3.059650
MYBC1 0.041968 0.265499 0.324798 3 3.632265
ICE1 0.040846 0.308625 0.098383 3 3.447854
NST1 0.023067 0.004043 0.009434 3 3.036545
AL5 0.013921 0.043127 0.040431 3 3.097479
IDD11 0.010722 0.052561 0.018868 3 3.082150
HB20 0.010560 0.063342 0.039084 3 3.112986
LBD14 0.005394 0.012129 0.176550 3 3.194074
LBD29 0.004063 0.004043 0.030997 3 3.039104
AT5G26610 0.003452 0.001348 0.020216 3 3.025015
NF-YA8 0.003221 0.001348 0.002695 3 3.007264
BSM 0.002855 0.002695 0.002695 3 3.008246
AT4G01280 0.002022 0.002695 0.004043 3 3.008761
LBD38 0.001828 0.030997 0.029650 3 3.062475
AT2G40200 0.001391 0.008086 0.021563 3 3.031041
AT1G61980 0.001348 0.001348 0.057951 3 3.060647
GATA23 0.001348 0.002695 0.044474 3 3.048518
AGL26 0.000378 0.013477 0.040431 3 3.054287
SCL21 0.000262 0.001348 0.021563 3 3.023173
AT5G46915 0.000218 0.001348 0.012129 3 3.013695
AtHB23 0.000120 0.029650 0.040431 3 3.070201
NF-YB2 0.000102 0.079515 0.026954 3 3.106571
GATA4 0.000091 0.002695 0.006739 3 3.009525
AT2G41710 0.000089 0.004043 0.082210 3 3.086342
AT1G44810 0.000060 0.028302 0.045822 3 3.074184
AT4G30180 0.000015 0.001348 0.016173 3 3.017535
HDA3 0.000004 0.008086 0.068733 3 3.076823
In [44]:
## Procambium
celltype = 'pro'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[44]:
pro_betweenness_centrality pro_out_centrality pro_in_centrality centrality_count centrality_sum
AT3G06590 0.967585 0.148629 0.102453 3 4.218667
BZIP24 0.961902 0.017316 0.020202 3 3.999420
HB18 0.893226 0.109668 0.072150 3 4.075044
AT4G23860 0.796827 0.007215 0.004329 3 3.808371
AT1G26610 0.738731 0.011544 0.005772 3 3.756047
AT4G03250 0.437924 0.007215 0.014430 3 3.459569
HSFB4 0.331490 0.051948 0.030303 3 3.413741
ARR15 0.306742 0.001443 0.004329 3 3.312514
TTR1 0.200075 0.001443 0.002886 3 3.204404
RGL1 0.083976 0.028860 0.004329 3 3.117165
AT1G69570 0.080176 0.007215 0.008658 3 3.096049
HAT3 0.061941 0.090909 0.060606 3 3.213456
AT2G17150 0.041722 0.007215 0.001443 3 3.050380
ZML1 0.019099 0.017316 0.012987 3 3.049402
SHY2 0.014601 0.046176 0.030303 3 3.091080
AGL58 0.013043 0.036075 0.008658 3 3.057776
DOF1 0.011901 0.025974 0.073593 3 3.111468
MYB60 0.009179 0.007215 0.050505 3 3.066899
MYB10 0.007198 0.001443 0.017316 3 3.025957
HAT1 0.004848 0.046176 0.041847 3 3.092871
BZR1 0.002901 0.023088 0.025974 3 3.051963
AT4G27900 0.002880 0.002886 0.004329 3 3.010095
CRF12 0.002673 0.015873 0.024531 3 3.043077
AT2G34450 0.001879 0.001443 0.002886 3 3.006208
LRP1 0.001733 0.010101 0.017316 3 3.029150
BZIP61 0.001629 0.014430 0.063492 3 3.079551
AT4G27240 0.001458 0.025974 0.027417 3 3.054849
BES1 0.000584 0.057720 0.027417 3 3.085721
AT3G50650 0.000571 0.015873 0.004329 3 3.020773
AT2G03470 0.000461 0.002886 0.011544 3 3.014891
TCP20 0.000423 0.046176 0.021645 3 3.068244
TCP8 0.000402 0.027417 0.023088 3 3.050908
NF-YB10 0.000156 0.031746 0.031746 3 3.063648
CHR38 0.000102 0.021645 0.004329 3 3.026076
PIE1 0.000100 0.010101 0.010101 3 3.020302
NF-YA4 0.000098 0.017316 0.012987 3 3.030401
AT5G38840 0.000035 0.012987 0.008658 3 3.021680
AT2G38950 0.000017 0.021645 0.025974 3 3.047636
AL3 0.000006 0.015873 0.008658 3 3.024537
AT5G13780 0.000006 0.012987 0.033189 3 3.046182
AT2G39020 0.000004 0.017316 0.020202 3 3.037522
HSF3 0.000002 0.014430 0.002886 3 3.017318
In [45]:
## Xylem
celltype = 'xyl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[45]:
xyl_betweenness_centrality xyl_out_centrality xyl_in_centrality centrality_count centrality_sum
OFP10 0.969356 0.123626 0.131868 3 4.224851
CRF6 0.969129 0.120879 0.079670 3 4.169679
MYB83 0.967744 0.524725 0.365385 3 4.857854
MYB32 0.919399 0.131868 0.071429 3 4.122695
AT1G66810 0.913488 0.461538 0.250000 3 4.625026
BEE2 0.837768 0.090659 0.214286 3 4.142713
AT1G68200 0.835732 0.087912 0.403846 3 4.327491
VND6 0.757931 0.159341 0.162088 3 4.079360
VND1 0.756546 0.112637 0.222527 3 4.091711
MYB46 0.751294 0.645604 0.810440 3 5.207338
HB31 0.748963 0.041209 0.120879 3 3.911051
AT3G51470 0.741789 0.016484 0.052198 3 3.810470
AT1G24610 0.677186 0.247253 0.054945 3 3.979384
VND7 0.643266 0.598901 0.527473 3 4.769639
AT4G16610 0.526617 0.129121 0.156593 3 3.812332
MMD1 0.459117 0.052198 0.035714 3 3.547029
ZHD3 0.433392 0.546703 0.337912 3 4.318008
VND4 0.265371 0.582418 0.563187 3 4.410975
MYB52 0.212484 0.546703 0.189560 3 3.948748
GATA6 0.208806 0.076923 0.035714 3 3.321444
AT2G20100 0.206680 0.230769 0.063187 3 3.500636
LBD31 0.195744 0.532967 0.274725 3 4.003436
VND5 0.143886 0.615385 0.442308 3 4.201579
NAC060 0.113182 0.140110 0.178571 3 3.431864
BZIP17 0.106416 0.030220 0.008242 3 3.144878
ERF9 0.077566 0.057692 0.002747 3 3.138006
AT3G22100 0.052569 0.016484 0.002747 3 3.071799
LBD18 0.050941 0.620879 0.406593 3 4.078414
HB34 0.043305 0.118132 0.131868 3 3.293305
JLO 0.039968 0.631868 0.310440 3 3.982275
FRS9 0.036668 0.005495 0.010989 3 3.053151
SHP1 0.025391 0.010989 0.184066 3 3.220446
AT5G25470 0.023015 0.087912 0.098901 3 3.209828
MYB99 0.018724 0.016484 0.208791 3 3.243998
AT1G24040 0.004730 0.173077 0.043956 3 3.221763
AT5G46910 0.002619 0.296703 0.118132 3 3.417454
AT3G04930 0.002437 0.008242 0.030220 3 3.040898
LDL2 0.001544 0.002747 0.068681 3 3.072972
PRR9 0.001317 0.027473 0.035714 3 3.064504
AT2G36480 0.000954 0.002747 0.021978 3 3.025679
AL2 0.000810 0.041209 0.027473 3 3.069491
E2F3 0.000136 0.005495 0.010989 3 3.016620
AT3G45880 0.000083 0.126374 0.027473 3 3.153929
In [46]:
## Phloem
celltype = 'phl'
cs = tf_spec[tf_spec['tf_occurance']==1][[celltype+'_betweenness_centrality', celltype+'_out_centrality',celltype+'_in_centrality']]
cso = (cs > 0)
cs['centrality_count'] = cso.sum(axis=1)
cs['centrality_sum'] = cs.sum(axis=1)
cs[cs['centrality_count']==3].sort_values(['centrality_count',celltype+'_betweenness_centrality'], ascending=False)
Out[46]:
phl_betweenness_centrality phl_out_centrality phl_in_centrality centrality_count centrality_sum
KNAT2 0.959314 0.030637 0.036765 3 4.026716
LDL1 0.887614 0.012255 0.011029 3 3.910899
PIF3 0.837399 0.074755 0.037990 3 3.950144
BHLH101 0.766294 0.019608 0.018382 3 3.804284
AGL80 0.728083 0.018382 0.017157 3 3.763622
GATA20 0.696991 0.131127 0.166667 3 3.994785
FRS11 0.696979 0.001225 0.008578 3 3.706783
APRR2 0.648838 0.056373 0.013480 3 3.718691
FLP 0.635148 0.060049 0.182598 3 3.877795
AT4G37180 0.435929 0.237745 0.223039 3 3.896713
AT1G55750 0.431858 0.011029 0.024510 3 3.467397
AT5G63700 0.365342 0.317402 0.286765 3 3.969509
JAZ11 0.255876 0.062500 0.033088 3 3.351465
AT2G45460 0.201431 0.009804 0.015931 3 3.227167
BBX29 0.186423 0.008578 0.002451 3 3.197453
EMB2219 0.076186 0.002451 0.004902 3 3.083539
REM22 0.050953 0.012255 0.064951 3 3.128159
SIGC 0.034171 0.020833 0.019608 3 3.074612
NAC020 0.013046 0.189951 0.321078 3 3.524075
AGL15 0.011790 0.172794 0.207108 3 3.391692
AT5G44080 0.008048 0.011029 0.025735 3 3.044812
AT2G20280 0.006467 0.045343 0.028186 3 3.079997
AT1G03150 0.004649 0.026961 0.006127 3 3.037738
AT4G25610 0.004490 0.008578 0.001225 3 3.014294
AT5G63080 0.002607 0.022059 0.014706 3 3.039372
WRKY20 0.002433 0.023284 0.034314 3 3.060031
ETL1 0.002422 0.026961 0.015931 3 3.045315
NAC086 0.001367 0.066176 0.074755 3 3.142298
LUH 0.001216 0.051471 0.023284 3 3.075971
AN3 0.000922 0.003676 0.001225 3 3.005824
PCFS4 0.000304 0.018382 0.035539 3 3.054225
AT1G10610 0.000298 0.008578 0.030637 3 3.039513
DDL 0.000256 0.002451 0.006127 3 3.008834
AT1G10120 0.000217 0.024510 0.050245 3 3.074971
NAC045 0.000146 0.042892 0.061275 3 3.104313
GBF2 0.000128 0.042892 0.036765 3 3.079785
BPC3 0.000116 0.012255 0.024510 3 3.036880
ROS4 0.000104 0.034314 0.025735 3 3.060153
AT2G40620 0.000093 0.008578 0.040441 3 3.049113
BPEp 0.000086 0.026961 0.014706 3 3.041752
PAT1 0.000084 0.036765 0.033088 3 3.069937
SOL1 0.000068 0.042892 0.033088 3 3.076048
AT3G11450 0.000029 0.037990 0.018382 3 3.056401
AT5G63280 0.000011 0.018382 0.009804 3 3.028197
AT4G25210 0.000003 0.039216 0.039216 3 3.078434
AT3G20010 0.000003 0.019608 0.015931 3 3.035542
AT1G75510 0.000002 0.040441 0.036765 3 3.077207
MBD13 0.000002 0.004902 0.028186 3 3.033090
AT2G37650 0.000002 0.013480 0.002451 3 3.015933
In [47]:
tf_spec.to_csv('scRNAseq_TF_GRN_centrality_t7-t9_zscore3.csv', index=True)
In [48]:
tf_spec
Out[48]:
tf_occurance atri_degree_centrality atri_out_centrality atri_in_centrality atri_betweenness_centrality atri_closeness_centrality atri_eigenvector_centrality tri_degree_centrality tri_out_centrality tri_in_centrality ... phl_in_centrality phl_betweenness_centrality phl_closeness_centrality phl_eigenvector_centrality col_degree_centrality col_out_centrality col_in_centrality col_betweenness_centrality col_closeness_centrality col_eigenvector_centrality
AT5G57150 7.0 0.070664 0.023555 0.047109 0.000018 0.000227 0.042571 0.000000 0.000000 0.000000 ... 0.056373 0.000983 0.000371 0.044610 0.013228 0.007937 0.005291 0.000058 0.000149 0.006192
HB-1 7.0 0.117773 0.070664 0.047109 0.000924 0.000198 0.053631 0.060606 0.035985 0.024621 ... 0.053922 0.000827 0.000362 0.048375 0.228836 0.099206 0.129630 0.000827 0.000330 0.062569
KNAT7 7.0 0.481799 0.237687 0.244111 0.356935 0.000273 0.127985 0.017045 0.009470 0.007576 ... 0.000000 0.000000 0.000000 0.000000 0.228836 0.126984 0.101852 0.001228 0.000291 0.060164
WLIM1 7.0 0.154176 0.109208 0.044968 0.005652 0.000237 0.061079 0.100379 0.066288 0.034091 ... 0.063725 0.002138 0.000383 0.052749 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
HAT22 7.0 0.092077 0.068522 0.023555 0.002109 0.000232 0.047758 0.522727 0.346591 0.176136 ... 0.011029 0.000000 0.000221 0.014375 0.201058 0.091270 0.109788 0.001246 0.000329 0.056127
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
PLT1 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.006614 0.001323 0.005291 0.000000 0.000148 0.003532
CIB5 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.005291 0.001323 0.003968 0.000000 0.000131 0.003218
AT4G34290 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.015873 0.002646 0.013228 0.000000 0.000194 0.007830
TAF12 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.003968 0.002646 0.001323 0.000000 0.000114 0.001528
AT5G44260 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.000000 0.000000 0.000000 0.000000 0.002646 0.001323 0.001323 0.000000 0.000101 0.000960

1349 rows × 61 columns

In [ ]: